Please use this identifier to cite or link to this item:
|Title:||Prediction of RNA-binding proteins from primary sequence by a support vector machine approach||Authors:||Han, L.Y.
Support vector machine
|Issue Date:||Mar-2004||Citation:||Han, L.Y., Cai, C.Z., Lo, S.L., Chung, M.C.M., Chen, Y.Z. (2004-03). Prediction of RNA-binding proteins from primary sequence by a support vector machine approach. RNA 10 (3) : 355-368. ScholarBank@NUS Repository. https://doi.org/10.1261/rna.5890304||Abstract:||Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein-protein interactions. But insufficient attention has been paid to the prediction of protein-RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent evaluation set show a prediction accuracy of 94.1%, 79.3%, and 94.1% for rRNA-, mRNA-, and tRNA-binding proteins, and 98.7%, 96.5%, and 99.9% for non-rRNA-, non-mRNA-, and non-tRNA-binding proteins, respectively. The SVM classification system was further tested on a small class of snRNA-binding proteins with only 60 available sequences. The prediction accuracy is 40.0% and 99.9% for snRNA-binding and non-snRNA-binding proteins, indicating a need for a sufficient number of proteins to train SVM. The SVM classification systems trained in this work were added to our Web-based protein functional classification software SVMProt, at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi. Our study suggests the potential of SVM as a useful tool for facilitating the prediction of protein-RNA interactions.||Source Title:||RNA||URI:||http://scholarbank.nus.edu.sg/handle/10635/53100||ISSN:||13558382||DOI:||10.1261/rna.5890304|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jun 27, 2020
WEB OF SCIENCETM
checked on Jun 19, 2020
checked on Jun 29, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.